WAMDM的全称是"Web And Mobile Data Management"(即网络与移动数据管理实验室), 它是孟小峰教授所领导的一个研究型实验室，附属于数据工程与知识工程教育部重点实验室以及中国人民大学 信息学院 计算机科学与技术系。
WAMDM实验室的研究兴趣是如何使数据库技术能更好地适应和融入网络和移动的计算环境。我们实验室的研究风格有两个方面：既有理论研究，也有实际系统。这样保证了我们的研究都是有实用价值的。创新数据系统研究是我们的目标!
新世纪以来，数据库界普遍面临的一个问题是，在传统的数据库技术成熟之后，数据库研究应向何处去？凭借自己对当时技术趋势的判断，我将研究目标定位在解决数据库技术与Web计算和移动计算交叉结合所产生的挑战性问题，即结构多样的Web数据管理，半结构化XML数据的管理，以及移动环境下的数据管理问题，并创立了"网络与移动数据管理实验室（Web and Mobile Data Management）"，致力于这方面的研究，取得了一些国内外所共知的研究成果。我把这一阶段的研究概括为创新数据管理研究1.0。自2011年又是一个十年的伊始，我们在思索实验室下一个十年的研究布局。我们不难发现数据库技术的变革（其实任何信息技术亦如此）主要来自三方面的驱动力，即：计算模式，硬件技术，应用模式的不断创新。基于三方面驱动力的需求，我们把对下一个十年的研究概括为创新数据管理研究2.0，具体包含如下的研究方向：（1）闪存数据库系统的研究。它来自硬件技术变革的驱动力，其研究目标是针对闪存硬件特性、灵活的应用模式和传统数据库技术的不足，研究全新的闪存数据库管理技术；（2）云数据库系统的研究。它来自计算模式变革的驱动力，其研究目标是实现一种具有灵活配置、高可用性、高容错性、可扩展性和高性能的云数据库系统；（3）Web与社会计算的研究。它来自应用模式变革的驱动力，其研究目标是把社会计算的方法引入Web数据管理，解决Web信息的可信和隐私保护问题；（4）Mobile与隐私保护研究。它来自应用模式变革的驱动力，即Mobile Web需求日益迫切，其研究目标是解决移动搜索、隐私保护等关键问题；（5）纯XML数据库系统研制。过去10年我们系统研究了纯XML数据库技术，获中国计算机学会"王选奖"，我们将寻求技术转移的途径，进行产业化尝试。
本网站包含了目前正在进行中的研究项目，以及我们实验室里的人员信息。您可以查看我们实验室每周研讨会和实验室年度报告信息。此外，您还可以查看以下相关站点：
实验室热点新闻
实验室研讨会
2019-03-29 Knowledge Representation for Emotion Intelligence（ICDE2019 PhD
Symposium预） by王硕
Abstract: Emotion intelligence (EI) is a traditional topic for psychology,
sociology, biology and medical science. Because emotion is related with the personality, interpersonal effect,
social function, disease treatment, etc. Analyzing the emotion from the Web data by computer technology
becomes more and more popular, and the scientists from the non-computer domains need more helpful computing
models to deal with professional problems that are not traditional for computer science. Knowledge
representation is a basic and possible solution as a bridge between emotion intelligence and artific ial
intelligence. For the sentiment words, word embedding can map the words to vectors that represent the semantic
context of the words. Sentiment embedding based on the word embedding can capture both semantics and the
emotion information. We have introduced two kinds of improving embedding methods (MEC and Emo2Vec) for the
sentiment words embedding. For emotion structure based on the psychology of emotion, knowledge graph can
represent the cognitive relations between different emotion types. The same emotional expressions can affect
the reaction and behaviors of the recipient in different ways due to factors such as social relations,
information processing, time pressure, etc. Knowledge graph can represent these complicated situations as the
relations between the entities and attributes. Based on this graph, we make the inference or prediction of the
emotion influence on decision making.
2019-03-29 EMT: A Tail-Oriented Method for Specific Domain Knowledge Graph
Completion（PAKDD预） by张祎
Abstract: The basic unit of knowledge graph is triplet, including head entity,
relation and tail entity. Centering on knowledge graph, knowledge graph completion has attracted more and more
attention and made great progress. However, these models are all verified by open domain data sets. When
applied in specific domain case, they will be challenged by practical data distributions. For example, due to
poor presentation of tail entities caused by their relation-oriented feature, they can not deal with the
completion of enzyme knowledge graph. Inspired by question answering and rectilinear propagation of lights,
this paper puts forward a tail-oriented method - Embedding for Multi-Tails knowledge graph (EMT).
Specifically, it first represents head and relation in question space; then, finishes projection to answer one
by tail-related matrix; finally, gets tail entity via translating operation in answer space. To overcome
time-space complexity of EMT, this paper includes two improved models: EMTv and EMTs. Taking some optimal
translation and composition models as baselines, link prediction and triplets classification on an enzyme
knowledge graph sample and Kinship proved our performance improvements, especially in tails prediction.
特邀报告
- 孟小峰. Keynote Speech. "大数据与社会计算促进交叉学科发展". 全国大数据与社会计算学术会议. 2017-08-12. 甘肃.[新闻链接]
- 孟小峰. Keynote Speech. "大规模隐私泄露问题规模与挑战".2017（第二届）中国隐私保护学术会议暨2017中国保密技术交流大会隐私保护论坛. 2017-08-04.贵阳.[新闻链接]
- 孟小峰. Keynote Speech. "大数据管理系统实践与展望". 第四届科学数据大会. 2017-08-02. 云南. 北京.[新闻链接]
- 孟小峰. Keynote Speech. "大数据治理中的隐私保护". （2016）首届中国隐私保护学术会议. 2016-11-07. 北京.[新闻链接]
- 孟小峰.Keynote Speech. “数据开放与隐私保护”. 2016年保密技术交流大会暨产品博览会—商业秘密与个人隐私保护技术论坛.2016-10-14.青岛.[新闻链接]
- 孟小峰. Keynote Speech. "科学大数据管理系统的挑战". 第三届科学数据大会. 2016-06-25. 上海.州.北京.[新闻链接]
- 孟小峰. Keynote Speech. "大数据隐私保护技术". 第四届密码学与云计算安全国际研讨会（CCCS2016）. 2016-06-17. 广州.北京.[新闻链接]
- 孟小峰. Keynote Speech. "大数据管理系统的发展与机遇". 第七届中国数据库技术大会. 2016-05-12. 北京.[新闻链接]
- 孟小峰. Keynote Speech. "大数据管理：问题与思考". 江西景德镇陶瓷大学. 2016-05-06. 景德镇.[新闻链接]
- 孟小峰. Invited Talk. "大数据融合". 江西财经大学. 2016-05-05. 南昌.[新闻链接]
- 孟小峰. Invited Talk. "大数据存储和隐私". 大数据论坛. 北京工业大学. 2016-01-18. 北京.6. 武汉.[新闻链接]
- 孟小峰. Keynote Speech. "位置大数据隐私保护". 2015"泛在测绘与位置大数据应用"国际工程论坛. 2015-11-06. 武汉.[新闻链接]
- 孟小峰. Keynote Speech. "面向国家安全的大数据隐私管理". NSFC "国家安全管理"双清论坛. 2015-05-06. 北京.[新闻链接]
- 孟小峰. Keynote speech. "大数据隐私管理". CNCC 2014“数据开放与隐私管理”专题论坛. 2014-10-25. 郑州. [新闻链接]
实验室新闻
[2017-09-15]孟小峰教授应邀参加CCF YOCSEF走进Springer Nature活动并作特邀演讲[详细]
[2017-08-22]孟小峰教授应邀参加“大数据管理丛书”英文版版权输出签约仪[详细]
[2017-01-05] 大规模知识图谱构建与应用研讨会在人大成功召开[详细]
[2016-12-09] 面向新型硬件的大数据管理研讨会在人大成功召开[详细]
[2016-11-07] WAMDM实验室举办首届全国隐私保护学术会议（ChinaPrivacy 2016）[详细]
[2015-12-02] WAMDM实验室举办大数据体系结构研讨会[详细]
[2015-11-06] 孟小峰教授应邀参加2015“泛在测绘与位置大数据应用”国际工程论坛并做特邀报告[详细]
[2015-10-29] 孟小峰教授访问普渡大学签署合作研究协议[详细]
[2015-07-17] 孟小峰教授应CCF YOCSEF邀请在天津大学计算机学院作报告[详细]
[2015-05-14] WAMDM实验室举办大数据隐私管理研讨会[详细]
[2015-04-08] WAMDM实验室举办复杂关联数据管理研讨会[详细]
[2015-01-20] 美国普渡大学Elisa Bertino教授访问网络与移动数据管理实验室[详细]
[2014-10-30] 孟小峰教授主持CNCC 专题论坛“数据开放与隐私管理”并作主题报告。[详细]
[2014-10-28] 孟小峰教授论文入选“领跑者5000——中国精品科技期刊顶尖学术论文”。[详细]
[2014-05-07] 孟小峰教授应邀在2014高等教育信息化创新论坛作主题报告。[详细]
[2014-04-16] CCF@U193：孟小峰走进北京工商大学。[详细]
[2014-01-06] 孟小峰教授当选中国计算机学会会士。[详细]
最近发表的论文：
C. Yang, Z. Du, X. Meng, et al. A Frequency Scaling based Performance Indicator Framework for Big Data
Systems[C]. Accepted for the 24th International Conference on Database Systems for Advanced Applications
(DASFAA), 2019, Chiang Mai, Thailand.
Yi Zhang, Zhijuan Du, Xiaofeng Meng. EMT: A Tail-Oriented Method for Specific Domain Knowledge Graph
Completion[C]. Accepted for Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD), 2019,Macau,
China.
Shuo Wang,Xiaofeng Meng. Konwledge Representation for Emotion Intelligence. Accepted for the 35th IEEE
International Conference on Data Engineering (ICDE) ,2019.Macau SAR, China.(PhD Symposium)
Zhiqiang Duan, Chen Yang, Yongjie Du, Xiaofeng Meng, et al.SciDetector: Scientific Event Discovery by Tracking
Variable Source Data Streaming[C].Accepted for International Conference on Data Engineering (ICDE) ,2019,SAR,
China.(Demo)
Q. Ye, H. Hu, X. Meng, et al. PrivKV: Key-Value Data Collection with Local Differential Privacy[C].
Proceedings of IEEE Symposium on Security and Privacy (S&P), IEEE, pages: 294-308, 2019, San Francisco,
USA.[PDF]
C. Yang, X. Meng, Z. Du. Cloud based Real-Time and Low Latency Scientific Event Analysis[C]. Proceedings of
the IEEE International Conference on Big Data (BigData), pages: 498-507,2018,Seattle, WA, USA.[PDF]
C. Yang, X. Meng, Z. Du. AstroServ: A Distributed Database for Serving Large-Scale Full Life-Cycle
Astronomical Data[C]. Proceedings of the first International Conference on Big Scientific Data Management
(BigSDM), vol:abs/1811.10861, 2018, Beijing, China.[PDF]
C. Yang, X. Meng, Z. Du. Data Management in Time-Domain Astronomy: Requirements and Challenges[C]. Proceedings
of the first International Conference on Big Scientific Data Management (BigSDM), vol:abs/1811.10855,2018,
Beijing, China.[PDF]
Y. Du, Chen Yang, Xiaofeng Meng. Real-Time Query Enabled by Variable Precision in Astronomy[C]. Proceedings of
the first International Conference on Big Scientific Data Management (BigSDM), 2018, Beijing, China.[PDF]
Z. Duan, C. Yang, X. Meng. Continuous Cross Identification in Large-scale Dynamic Astronomical Data Flow[C].
Proceedings of the first International Conference on Big Scientific Data Management (BigSDM), 2018, Beijing,
China.[PDF]
S. Wang, X. Meng. Multi-Emotion Category Improving Embedding for Sentiment Classification[C]. Proceedings of
the 27th ACM International Conference on Information and Knowledge Management. ACM (CIKM), pages: 1719-1722,
2018, Turin, Italy.[PDF]
X. Meng, Z. Hao, J. Li, Y. Zhang, et al. ScholarSpace: Academic Search in China[C]. Proceedings of ACM
Woodstock conference (WOODSTOCK’18). ACM, 2018, New York, USA.[PDF]
Y. Zhang, Z. Hao, J. Li, X. Meng, & S. Wang. KGBuilder: A System for Large-Scale Scientific Domain Knowledge
Graph Building[C]. Proceedings of 3rd international workshop on Biomedical Informatics with Optimization and
Machine Learning (Boom), in conjunction with 27th International Joint Conference on Artificial Intelligence.
(IJCAI), 2018, Stockholm, Sweden.[PDF]
Q. Wang, X. Meng. Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model[C].
Proceedings of the BioNLP 2018 workshop, pages:147-150, 2018, Melbourne.[PDF]
S. Wang, Z. Hao, X. Meng, et al. Scholar Graph: A Chinese Knowledge Graph of Chinese Scholars[C]. Proceedings
of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018), 2018, Miyazaki,
Japan.[PDF]
霍 峥 , 孟 小 峰 . 一 种 满 足 差 分 隐 私 的 轨 迹 数 据 发 布 方 法 [J]. 计 算 机 学 报 ,
Vol41(2):400-412, 2018.[PDF]
张啸剑, 金凯忠, 孟小峰. 基于自适应网格的隐私空间分割方法[J]. 计算机研究与发展, Vol55(6):1143-1156,2018.[PDF]
张啸剑, 付聪聪, 孟小峰. 面向人脸图像发布的差分隐私保护[J]. 中国图象图形学报, Vol23(9):1305-1315, 2018.[PDF]
叶青青, 孟小峰, 朱敏杰, 等. 本地化差分隐私研究综述[J]. 软件学报,Vol29(7):1981-2005,2018.[PDF]
王春凯,孟小峰. 应对倾斜数据流在线连接方法[J]. 软件学报, Vol29(3):869-882, 2018.[PDF]
C. Wang, X. Meng, et al. Automating Characterization Deployment in Distributed Data Stream Management
Systems[J]. Proceedings of IEEE Transactions on Knowledge and Data Engineering(TKDE),pages:2669--2681,2017,San
Diego, California, USA. (Full Paper).[PDF]
C. Yang, Q. Guo, X. Meng, et al. Revisiting Performance in Big Data Systems: A Resource Decoupling Approach.
Proceedings of ACM Symposium on Cloud Computing(SoCC),pages:639,2017, Santa Clara , CA.(Poster).[PDF]
C. Wang, X. Meng. Partitioning Road Network Streams Based on Runtime Correlation Discovery[C]. Proceedings of
the 18th IEEE International Conference on the Mobile Data Management (MDM), pages: 272-277,2017, Daejeon,
South Korea.[PDF]
Y. Li, X. Meng, Zhang Q, et al. Common patterns of online collective attention flow[J]. Science China
Information Sciences, Vol 60(5): 059102:1-3, 2017.[PDF]
Z. Weng, Q. Guo, C. Wang, et al. AdaStorm: Resource Efficient Storm with Adaptive Configuration[C].
Proceedings of the 33rd International Conference on Data Engineering (ICDE) , pages: 1363-1364, 2017, San
Diego, CA.[PDF]
Z. Du, Z. Hao, X. Meng, et al. CirE: Circular Embeddings of Knowledge Graphs[C]. Proceedings of the
International Conference on Database Systems for Advanced Applications (DASFFA), pages:148-162, 2017, Suzhou,
China.[PDF]
Z. Hao, Z. Wang, X. Meng, et al. Semantic Definition Ranking[C]. Proceedings of the International Conference
on Database Systems for Advanced Applications (DASFFA), pages:153-168, 2017, Suzhou, China.[PDF]
S. Guo, X. Meng. Density Peaks Clustering with Differential Privacy[C]. Proceedings of the 8th Biennial
Conference on Innovative Data Systems Research(CIDR),2017, Chaminade, CA.
Wang C, Meng X, Guo Q, et al. OrientStream: A Framework for Dynamic Resource Allocation in Distributed Data
Stream Management Systems[C].Proceedings of the 25th ACM International on Conference on Information and
Knowledge Management(CIKM),pages: 2281-2286, 2016,Indianapolis,IN.[PDF]
Ma Y, Meng X, Wang S. Parallel similarity joins on massive high‐dimensional data using MapReduce[J].
Concurrency and Computation: Practice and Experience, Vol 28(1): 166-183, 2016.
J.Wang,Z. Guo, X.Meng. An Efficient Design and Implementation of Multi-Level Cache for Database Systems [C].
Proceedings of the20th International Conferenceon Database Systems for Advanced Applications (DASFAA),pages:
160-174, 2015,Hanoi, Vietnam.[PDF]
J.Wang,Z. Guo, X. Meng. SASS: A High-Performance Key-Value Store Design for Massive Hybrid Storag[C].
Proceedings of the20th International Conferenceon Database Systems for Advanced Applications (DASFAA),pages:
145-159,2015,Hanoi, Vietnam.[PDF]
L.Wang, X.Meng, H. Hu, et al. Bichromatic Reverse Nearest Neighbor Query without Information Leakage. [C].
Proceedings of the20th International Conferenceon Database Systems for Advanced Applications
(DASFAA),pages:609-624,2015,Hanoi, Vietnam.[PDF]
Y. Ma, X. Meng. Set similarity join on massive probabilistic data using MapReduce. Distributed and Parallel
Databases. Vol 32(3): 447-464, 2014.[PDF]
Y. Gan, W. Lai, X. Meng. Optimizing Database Operators by Exploiting Internal Parallelism of Solid State
Drives. IEEE Data Eng. Bull. Vol 37(2): 12-18, 2014.[PDF]
X. Zhang, R. Chen, J. Xu, X. Meng. Towards Accurate Histogram Publication under Differential Privacy.
Accepted for publication in Proceedings of the 14th SIAM International Conference on Data Mining (SDM 2014):
587-595, Philadelphia, Pennsylvania, USA. (Full Paper)[PDF]
J. Zhou, Z. Bao, W. Wang, J. Zhao, X. Meng: Efficient Query Processing for XML Keyword Queries based on the
IDList Index. Accepted by VLDBJ. Vol 23(1): 25-50, 2014.[PDF]
Z. Huo, X. Meng, R. Zhang: Feel Free to Check In: Privacy Alert against Hidden Location Inference Attacks in
GeoSN. In Processings of the 18th International Conference on Database Systems for Advanced Applications
(DASFAA 2013): 377-491. April 22-25, 2013, Wuhan, China. (Regular paper)[PDF]
X. Zhang, X. Meng, R. Chen: Differential Private Set-Valued Data Release against Incremental Updates. In
Proceedings of the 18th International Conference on Database Systems for Advanced Applications (DASFAA 2013):
392-406. April 22-25, 2013, Wuhan, China. (Regular paper)[PDF]
Y. Gan, X. Meng, Y. Shi: COLA: A Cloud-based System for Online Aggregation (Demonstration). In Proceedings
of the 29th International Conference on Data Engineering(ICDE2013): 1368-1371, April 8-12, 2013, Brisbane,
Australia.[PDF]
W. Cao, D. SHASHA: AppSleuth: a Tool for Database Tuning at the Application Level. In Proceedings of the
16th International Conference on Extending Database Technology (EDBT2013): 589-600. March 18-22, Genoa,
Italy.[PDF]
D. SHASHA, W .Cao: Tuning in Action (Demonstration) In Proceedings of the 16th International Conference on
Extending Database Technology (EDBT 2013): 737-740. March 18-22, Genoa, Italy.[PDF]
